173 citations · 184 across the 5 of their papers we have counts for
7 papers
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis
Yi Zhou, Parikshit Ram, Theodoros Salonidis +3
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a gener…
Selective Edge Computing for Mobile Analytics
Apostolos Galanopoulos, George Iosifidis, Theodoros Salonidis +1
An increasing number of mobile applications rely on Machine Learning (ML) routines for analyzing data. Executing such tasks at the user devices saves the energy spent on transmitti…
Improving IoT Analytics through Selective Edge Execution
A. Galanopoulos, A. G. Tasiopoulos, G. Iosifidis +2
A large number of emerging IoT applications rely on machine learning routines for analyzing data. Executing such tasks at the user devices improves response time and economizes net…
Anonymizing Data for Privacy-Preserving Federated Learning
Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis +4
Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in h…
Differential Privacy-enabled Federated Learning for Sensitive Health Data
Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis +4
Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a central…
Maximum Lifetime Analytics in IoT Networks
Victor Valls, George Iosifidis, Theodoros Salonidis
This paper studies the problem of allocating bandwidth and computation resources to data analytics tasks in Internet of Things (IoT) networks. IoT nodes are powered by batteries, c…